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SHS Sector Network Meeting

Developing a Shared Outcome Framework for the Housing and Homelessness Sectors Project 2: Homelessness sector outcomes. SHS Sector Network Meeting. Lena Etuk , Sr Research Officer Karen Wilcox , Project Officer. 16 May 2018. Project overview.

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SHS Sector Network Meeting

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  1. Developing a Shared Outcome Framework for the Housing and Homelessness SectorsProject 2: Homelessness sector outcomes SHS Sector Network Meeting Lena Etuk, Sr Research Officer Karen Wilcox, Project Officer 16 May 2018

  2. Project overview In March 2017, CSI was engaged by Homelessness NSW to develop a shared outcomes framework for the housing and homelessness sector. The objectives were to: • Determine key outcomes for homelessness services • Identify and assess outcome indicators and measures • Engage and consult with stakeholders to prioritise indicators • Synthesise findings into a shared outcomes framework CSI’s scope was constrained by: • Focus on individual outcomes (vs. organisational, structural or system); those at risk of or currently experiencing homelessness • Identifying existing indicators / measures, not developing new tools • Not including process for organisations to collect or report the data

  3. Intention • Identify shared outcomes across the community housing (Project 1) and homelessness (Project 2) sectors • Learn what outcomes are important for the sectors • Learn what difference organisations hope to make for people • Provide a foundation for outcome measurement to help establish a shared learning culture with tools and resources to build readiness for outcome measurement • The focus is not outcome-based commissioning or contracting

  4. Developing our understanding

  5. next steps: important to share and refine Further consultation to: Learn about existing outcomes measurement practices within SHSs Map and incorporate existing data Learn about fit of outcomes to SHSs and clients Identify initiatives to test and pilot outcomes Start small and pilot test: Small-scale / focused tests Different contexts and settings Learn and adapt before scaling approach Develop a roadmap for outcome measurement: Clarity around purpose for outcome measurement and evaluation Identify resources and supports Capacity building and skills development Align with other projects

  6. Prioritised indicators: process • Identify a core set of indicators that the homelessness sector can agree on as important • An online survey with services that receive FACS funding and deliver homelessness services

  7. Prioritised indicators: participation • The survey was open for 10 days at the beginning of August 2017 • Response rate of 58% (98 out of 169) • All regions were represented • Sydney and the Hunter/New England regions were the most common areas • Services targeting women or children escaping family violence, families, single women, and indigenous people were most common

  8. Prioritised indicators: Products https://www.shssectordev.org.au/projects/outcomes/homelessness-shared-outcome-framework-project

  9. Priority Outcomes

  10. Priority indicators

  11. Priority indicators

  12. Priority indicators

  13. Sector Indicator Data Dictionary https://www.shssectordev.org.au/projects/outcomes/homelessness-shared-outcome-framework-project

  14. Indicator Data Dictionary: Layout Outcome for clients, resulting from their contact with service Priority indicator for the sector that is a measurable marker of the outcome The question or tool used to collect and measure data for this indicator How the indicator is defined when using this specific data collection tool The source of population data for this outcome A publication or URL for more information about this outcome indicator

  15. Home Outcomes

  16. Home Outcomes

  17. Home Outcomes

  18. Economic Outcomes

  19. Economic Outcomes

  20. Economic Outcomes

  21. Education Outcomes

  22. Health Outcomes

  23. Safety Outcomes

  24. Social & Community Outcomes

  25. Social & Community Outcomes

  26. Other Outcomes

  27. Other Outcomes

  28. Priority indicators feedback • What are your thoughts on the measures for the prioritised outcomes and indicators? • Do you foresee any challenges with these measures? If so, how would you address them? • What are the things you’re more worried/optimistic about regarding outcome measurement?

  29. Sector Indicator Data Collection Guide https://www.shssectordev.org.au/projects/outcomes/homelessness-shared-outcome-framework-project

  30. How to Collect Outcome Data The guide aims to: • Improve your knowledge of data collection • Improve the accuracy and reliability of the data collection process • Reduce risks around collection, storage and management • Help you identify skill, capability, and resource gaps

  31. Key Considerations in Data Collection • What do you need to know? • What resources do you have available? • What data do you already collect? • What is the setting for data collection and who will be responsible for collecting the data? • What are the ethical and privacy considerations?

  32. Options for Data Collection • Sampling approaches • Data collection methods • Data collection mechanisms

  33. 1. Sampling Approaches Population count • Data are collected from everyone in a “population” • Provides a true measure of prevalence or incidence in the population • Can be quite costly • You can do it by: • Collecting data from all service recipients, on entry • Collecting data from all service recipients, 12 months after exit Sample of the population • Data are collected from a subset of a population • Random subset • Non-random subset • Can save money • If population representativeness is desired, you’ll need to carefully design your sampling techniques • You can do it by: • Selecting a set of people at a given time or place to participate • Dividing people into groups based on different characteristics • Selecting people based on a quota of a characteristics Who has collected/is collecting population count data? Challenges/Successes? Who has collected/is collecting sample data? Challenges/Successes

  34. 2. Data Collection Methods Surveys • Involves asking people to provide written responses to questions • They write the answers themselves • Someone else writes their answers for them • Can be easily repeated to collect many data points • May suffer from bias due to non-response • Questions may be misinterpreted • You can apply this to: • Pre-/post- surveys • Collecting data from a large group Direct Observation • Involves collecting information by watching the subject in their environment • Provides real-life insight into people’s behaviours • Resource intensive to collect • Observation and analysis may suffer from bias • You can apply this to situations when: • Interviewing is culturally inappropriate • Behaviours or activities are really complex Who has collected/is collecting survey data? Challenges/Successes? Who has collected/is collecting data via direct observation? Challenges/Successes?

  35. 2. Data Collection Methods Interviews • Involves asking people to provide verbal responses to questions or prompts • Highly structured • Semi-structured • Unstructured • Allows for clarifying and probing responses • Can reveal unanticipated areas for exploration • Requires a skilled interviewer • Resource intensive to collect • May suffer from bias due to interviewer effects • You can apply this to: • The case work process Focus Groups • Structured discussions with a group of people that respond to open ended questions in their own words • Can explore the issues in-depth • Can save money • Responses may suffer from bias • Data will not be generalizable • You can apply this to situations when: • Trying to identify outcomes or results from an activity Who has collected/is collecting interview data? Challenges/Successes? Who has collected/is collecting focus group data? Challenges/Successes?

  36. 2. Data Collection Methods Administrative/secondary data • Involves using existing organisational/program records or data from an external source such as the Australian Bureau of Statistics or Australian Institute of Health and Welfare • Can save money • Quality of data may be poor • You can apply this to: • Times when you need information about your client from another service • Learning about the community’s overall outcomes Who has collected/is collecting administrative data? Challenges/Successes?

  37. 3. Mechanisms for Data collection • Online • Telephone • Face-to-face • Post • SMS message

  38. Re-Cap of next steps Further consultation to: Learn about existing outcomes measurement practices within SHSs Map and incorporate existing data Learn about fit of outcomes to SHSs and clients Identify initiatives to test and pilot outcomes Start small and pilot test: Small-scale / focused tests Different contexts and settings Learn and adapt before scaling approach Develop a roadmap for outcome measurement: Clarity around purpose for outcome measurement and evaluation Identify resources and supports Capacity building and skills development Align with other projects

  39. CONTACT DETAILS Lena Etuk Sr Research Officer P +61 2 8936 0929 E l.etuk@unsw.edu.au @CSIsocialimpact www.csi.edu.au Karen Wilcox Project Officer P +61 0425 680 158 E  kawilco@gmail.com

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